The ability to recognize speech of a speaker is a basic human auditory system function. However, this function is notoriously difficult to reproduce using previously known machine-listening technologies because spoken communication often occurs in adverse acoustic environments. The problem is also complicated because how a person speaks the same words often varies between different utterances. Nevertheless, the unimpaired human auditory system is able to recognize speech effectively and perceptually instantaneously.
As a previously known machine-listening process, speech recognition (and subsequent re-synthesis) often includes recognizing phonemes using statistical formalisms. Phonemes are a basic representation of information bearing vocalizations. However, the previously known approaches for detecting and recognizing phonemes have a number of drawbacks. First, for example, in order to improve performance, previously known neural network approaches are heavily dependent on language-specific models, which make such approaches language-dependent. Second, many of the previously known neural network approaches recognize phonemes too slowly for real-time and/or low-latency applications because they are reliant on look-ahead information in order to provide context. Third, previously known neural network approaches are becoming increasingly computationally complex, use ever-larger memory allocations, and yet remain functionally limited and highly inaccurate.
Due to increasing computational complexity and memory demands, previously known phoneme detection and recognition approaches are characterized by long delays and high power consumption. As such, these approaches are undesirable for low-power, real-time and/or low-latency devices, such as hearing aids and mobile devices (e.g., smartphones, wearable devices, etc.).